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arXiv 2025年

作者： Feng, Yifan Hu, Hao Hou, Xingliang Liu, Shiquan Ying, Shihui Du, Shaoyi Hu, Han Gao, Yue School of Software Tsinghua University Beijing100871 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China School of Software Engineering Xi’an Jiaotong University Xi’an710049 China Department of Mathematics School of Science Shanghai University Shanghai200000 China School of Information and Electronics Beijing Institute of Technology Beijing100871 China

Large language models (LLMs) have transformed various sectors, including education, finance, and medicine, by enhancing content generation and decision-making processes. However, their integration into the medical field is cautious due to hallucinations, instances where generated content deviates from factual accuracy, potentially leading to adverse outcomes. To address this, we introduce Hyper-RAG, a hypergraph-driven Retrieval-Augmented Generation method that comprehensively captures both pairwise and beyond-pairwise correlations in domain-specific knowledge, thereby mitigating hallucinations. Experiments on the NeurologyCrop dataset with six prominent LLMs demonstrated that Hyper-RAG improves accuracy by an average of 12.3% over direct LLM use and outperforms Graph RAG and Light RAG by 6.3% and 6.0%, respectively. Additionally, Hyper-RAG maintained stable performance with increasing query complexity, unlike existing methods which declined. Further validation across nine diverse datasets showed a 35.5% performance improvement over Light RAG using a selection-based assessment. The lightweight variant, Hyper-RAG-Lite, achieved twice the retrieval speed and a 3.3% performance boost compared with Light RAG. These results confirm Hyper-RAG’s effectiveness in enhancing LLM reliability and reducing hallucinations, making it a robust solution for high-stakes applications like medical diagnostics. Copyright © 2025, The Authors. All rights reserved.

关键词： Neurophysiology

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Granular conditional entropy-based attribute reduction for partially labeled data with proxy labels

arXiv

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arXiv 2021年

作者： Gao, Can Zhou, Jie Miao, Duoqian Yue, Xiaodong Wana, Jun College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Department of Computer Science and Technology Tongji University Shanghai201804 China School of Computer Engineering and Science Shanghai University Shanghai200444 China

Attribute reduction is one of the most important research topics in the theory of rough sets, and many rough sets-based attribute reduction methods have thus been presented. However, most of them are specifically designed for dealing with either labeled data or unlabeled data, while many real-world applications come in the form of partial supervision. In this paper, we propose a rough sets-based semi-supervised attribute reduction method for partially labeled data. Particularly, with the aid of prior class distribution information about data, we first develop a simple yet effective strategy to produce the proxy labels for unlabeled data. Then the concept of information granularity is integrated into the information-theoretic measure, based on which, a novel granular conditional entropy measure is proposed, and its monotonicity is proved in theory. Furthermore, a fast heuristic algorithm is provided to generate the optimal reduct of partially labeled data, which could accelerate the process of attribute reduction by removing irrelevant examples and excluding redundant attributes simultaneously. Extensive experiments conducted on UCI data sets demonstrate that the proposed semi-supervised attribute reduction method is promising and even compares favourably with the supervised methods on labeled data and unlabeled data with true labels in terms of classification performance. © 2021, CC BY-NC-SA.

关键词： Rough set theory

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High-Resolution Optical Fiber Shape Sensing of Continuum Robots: A Comparative Study *

High-Resolution Optical Fiber Shape Sensing of Continuum Rob...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Frederic Monet Shahriar Sefati Pierre Lorre Arthur Poiffaut Samuel Kadoury Mehran Armand Iulian Iordachita Raman Kashyap Department of Engineering Physics Polytechnique Montral Montreal Canada Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Department of Computer and Software Engineering Polytechnique Montral Montreal Canada

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Flexible medical instruments, such as Continuum Dexterous Manipulators (CDM), constitute an important class of tools for minimally invasive surgery. Accurate CDM shape reconstruction during surgery is of great importance, yet a challenging task. Fiber Bragg grating (FBG) sensors have demonstrated great potential in shape sensing and consequently tip position estimation of CDMs. However, due to the limited number of sensing locations, these sensors can only accurately recover basic shapes, and become unreliable in the presence of obstacles or many inflection points such as s-bends. Optical Frequency Domain Reflectometry (OFDR), on the other hand, can achieve much higher spatial resolution, and can therefore accurately reconstruct more complex shapes. Additionally, Random Optical Gratings by Ultraviolet laser Exposure (ROGUEs) can be written in the fibers to increase signal to noise ratio of the sensors. In this comparison study, the tip position error is used as a metric to compare both FBG and OFDR shape reconstructions for a 35 mm long CDM developed for orthopedic surgeries, using a pair of stereo cameras as ground truth. Three sets of experiments were conducted to measure the accuracy of each technique in various surgical scenarios. The tip position error for the OFDR (and FBG) technique was found to be 0.32 (0.83) mm in free-bending environment, 0.41 (0.80) mm when interacting with obstacles, and 0.45 (2.27) mm in s-bending. Moreover, the maximum tip position error remains sub-millimeter for the OFDR reconstruction, while it reaches 3.40 mm for FBG reconstruction. These results propose a cost-effective, robust and more accurate alternative to FBG sensors for reconstructing complex CDM shapes.

关键词： Shape Fiber gratings Optical sensors Robot sensing systems Spatial resolution

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Image super-resolution with an enhanced group convolutional neural network

arXiv

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arXiv 2022年

作者： Tian, Chunwei Yuan, Yixuan Zhang, Shichao Lin, Chia-Wen Zuo, Wangmeng Zhang, David School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China Department of Electrical Engineering City University of Hong Kong Hong Kong School of Computer Science and Engineering Central South University Hunan Changsha410083 China Department of Electrical Engineering the Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China Peng Cheng Laboratory Guangdong Shenzhen518055 China Guangdong Shenzhen518172 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. However, CNNs depend on deeper network architectures to improve performance of image super-resolution, which may increase computational cost in general. In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusing deep and wide channel features to extract more accurate low-frequency information in terms of correlations of different channels in single image super-resolution (SISR). Also, a signal enhancement operation in the ESRGCNN is useful to inherit more long-distance contextual information for resolving long-term dependency. An adaptive up-sampling operation is gathered into a CNN to obtain an image super-resolution model with low-resolution images of different sizes. Extensive experiments report that our ESRGCNN surpasses the state-of-the-arts in terms of SISR performance, complexity, execution speed, image quality evaluation and visual effect in SISR. Code is found at https://***/hellloxiaotian/ESRGCNN. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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An efficient artificial intelligence approach for early detection of cross-site scripting attacks

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Decision Analytics Journal 2024年 11卷

作者： Younas, Faizan Raza, Ali Thalji, Nisrean Abualigah, Laith Zitar, Raed Abu Jia, Heming Department of Computer Science & Information Technology The University Of Lahore Lahore 54000 Pakistan Department of Software Engineering The University Of Lahore Lahore 54000 Pakistan Department of Robotics and Artificial Intelligence Jadara University Irbid Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia MEU Research Unit Middle East University Amman 11831 Jordan Applied science research center Applied science private university Amman 11931 Jordan School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi Abu Dhabi 38044 United Arab Emirates School of Information Engineering Sanming University Sanming 365004 China

Cross-Site Scripting (XSS) attacks continue to pose a significant threat to web applications, compromising the security and integrity of user data. XSS is a web application vulnerability where malicious scripts are injected into websites, allowing attackers to execute arbitrary code in the victim's browser. The consequences of XSS attacks can be severe, ranging from financial losses to compromising sensitive user information. XSS attacks enable attackers to deface websites, distribute malware, or launch phishing campaigns, compromising the trust and reputation of affected organizations. This study proposes an efficient artificial intelligence approach for the early detection of XSS attacks, utilizing machine learning and deep learning approaches, including Long Short-Term Memory (LSTM). Additionally, advanced feature engineering techniques, such as the Term Frequency-Inverse Document Frequency (TFIDF), are applied and compared to evaluate results. We introduce a novel approach named LSTM-TFIDF (LSTF) for feature extraction, which combines temporal and TFIDF features from the cross-site scripting dataset, resulting in a new feature set. Extensive research experiments demonstrate that the random forest method achieved a high performance of 0.99, outperforming state-of-the-art approaches using the proposed features. A k-fold cross-validation mechanism is utilized to validate the performance of applied methods, and hyperparameter tuning further enhances the performance of XSS attack detection. We have applied Explainable Artificial Intelligence (XAI) to understand the interpretability and transparency of the proposed model in detecting XSS attacks. This study makes a valuable contribution to the growing body of knowledge on XSS attacks and provides an efficient model for developers and security practitioners to enhance the security of web applications. © 2024 The Author(s)

关键词： Artificial intelligence Cross-site scripting attacks Deep learning Feature engineering Feature fusion Machine learning

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A heterogeneous group CNN for image super-resolution

arXiv

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arXiv 2022年

作者： Tian, Chunwei Zhang, Yanning Zuo, Wangmeng Lin, Chia-Wen Zhang, David Yuan, Yixuan The School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China The School of Computer Science Northwestern Polytechnical University The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China The School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China The Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan Guangdong Shenzhen518172 China The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong

Convolutional neural networks (CNNs) have obtained remarkable performance via deep architectures. However, these CNNs often achieve poor robustness for image superresolution (SR) under complex scenes. In this paper, we present a heterogeneous group SR CNN (HGSRCNN) via leveraging structure information of different types to obtain a high-quality image. Specifically, each heterogeneous group block (HGB) of HGSRCNN uses a heterogeneous architecture containing a symmetric group convolutional block and a complementary convolutional block in a parallel way to enhance internal and external relations of different channels for facilitating richer low-frequency structure information of different types. To prevent appearance of obtained redundant features, a refinement block with signal enhancements in a serial way is designed to filter useless information. To prevent loss of original information, a multi-level enhancement mechanism guides a CNN to achieve a symmetric architecture for promoting expressive ability of HGSRCNN. Besides, a parallel up-sampling mechanism is developed to train a blind SR model. Extensive experiments illustrate that the proposed HGSRCNN has obtained excellent SR performance in terms of both quantitative and qualitative analysis. Codes can be accessed at https://***/hellloxiaotian/HGSRCNN. © 2022, CC BY-NC-SA.

关键词： Convolutional neural networks

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FDA: Feature decomposition and aggregation for robust airway segmentation

arXiv

引用

arXiv 2021年

作者： Zhang, Minghui Yu, Xin Zhang, Hanxiao Zheng, Hao Yu, Weihao Pan, Hong Cai, Xiangran Gu, Yun Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Department of Computer Science and Software Engineering Swinburne University of Technology VIC Australia Medical Image Centre The First Affiliated Hospital of Jinan University Guangzhou China Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai China

3D Convolutional Neural Networks (CNNs) have been widely adopted for airway segmentation. The performance of 3D CNNs is greatly influenced by the dataset while the public airway datasets are mainly clean CT scans with coarse annotation, thus difficult to be generalized to noisy CT scans (e.g. COVID-19 CT scans). In this work, we proposed a new dual-stream network to address the variability between the clean domain and noisy domain, which utilizes the clean CT scans and a small amount of labeled noisy CT scans for airway segmentation. We designed two different encoders to extract the transferable clean features and the unique noisy features separately, followed by two independent decoders. Further on, the transferable features are refined by the channel-wise feature recalibration and Signed Distance Map (SDM) regression. The feature recalibration module emphasizes critical features and the SDM pays more attention to the bronchi, which is beneficial to extracting the transferable topological features robust to the coarse labels. Extensive experimental results demonstrated the obvious improvement brought by our proposed method. Compared to other state-of-the-art transfer learning methods, our method accurately segmented more bronchi in the noisy CT scans. Copyright © 2021, The Authors. All rights reserved.

关键词： Computerized tomography

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Developing SPIM-TA: a maturity-level framework for systematic process improvement in software testing automation

引用

Ain Shams engineering Journal 2025年第8期16卷

作者： Muhammad Faisal Abrar Yasser Alharbi Muhammad Alsaffar Shah Hussain Muhammad Saqib Jawad Khan Youngmoon Lee College of Computer Science & Engineering University of Ha’il Ha’il 2440 Saudi Arabia Department of Information and Computer Science University of Ha’il Ha'il 2440 Saudi Arabia Department of Mathematics College of Science University of Ha’il Ha’il 2440 Saudi Arabia Department of Software Engineering University of Engineering & Technology Peshawar 25000 Pakistan School of Computing Gachon University Seongnam 13120 Republic of Korea Deparment of Robotics Hanyang University Ansan 15588 Republic of Korea

The growing complexity and demands for high-quality software underlined the need for robust automated software testing frameworks. However, lack of skilled personnel, cost of testing automation, and maintenance of script along with others oppose the widespread adoption of automation in testing. The work proposed by this study is in line with the development of Systematic Process Improvement Model for Testing Automation, abbreviated as SPIM-TA. The model is inspired from Capability Maturity Model Integration (CMMI) and software Outsourcing Vendor Readiness Model (SOVRM) maturity-level-based framework. The research was done based on a four-phased methodology. In the first phase, a systematic literature review identified 14 critical challenges in software testing automation with practices or solutions for each of these challenges. In the second round, a survey questionnaire is used to validate these findings in industrial context and extract more practical insights from case studies. This dual approach had ensured both academic rigor and the relevance of the practices with industry. The third one involved developing SPIM-TA by incorporating insights that come from the SLR and those results of the survey as well. The framework defines five maturity levels, systematically addressing the identified challenges and providing a structured pathway for organizations to enhance their automation processes. In the fourth phase, case studies of industrial applications were carried out to evaluate SPIM-TA’s efficiency. The applicability, scalability, and impact of the framework on testing automation in the real world were assessed using the Motorola Assessment Tool. The findings illustrate the systematic way SPIM-TA will help overcome the automation challenge, so that organizations can reach more advanced levels of maturity and efficiency in their testing processes. In the first place, the present study closes the gap between academia and industry but at the same time paves the fou

关键词： Automated software Testing SPIM-TA framework Systematic Process Improvement

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Robust and Precise Facial Landmark Detection by Self-Calibrated Pose Attention Network

arXiv

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arXiv 2021年

作者： Wan, Jun Xi, Hui Zhou, Jie Lai, Zhihui Pedrycz, Witold Wang, Xu Sun, Hang School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan430073 China College of Computer Science and Software Engineering Shen zhen University Shenzhen518060 China Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Department of Electrical & Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland College of Computer and Information Technology China Three Gorges University Yichang HuBei China

Current fully-supervised facial landmark detection methods have progressed rapidly and achieved remarkable performance. However, they still suffer when coping with faces under large poses and heavy occlusions for inaccurate facial shape constraints and insufficient labeled training samples. In this paper, we propose a semi-supervised framework, i.e., a Self-Calibrated Pose Attention Network (SCPAN) to achieve more robust and precise facial landmark detection in challenging scenarios. To be specific, a Boundary-Aware Landmark Intensity (BALI) field is proposed to model more effective facial shape constraints by fusing boundary and landmark intensity field information. Moreover, a Self-Calibrated Pose Attention (SCPA) model is designed to provide a self-learned objective function that enforces intermediate supervision without label information by introducing a self-calibrated mechanism and a pose attention mask. We show that by integrating the BALI fields and SCPA model into a novel self-calibrated pose attention network, more facial prior knowledge can be learned and the detection accuracy and robustness of our method for faces with large poses and heavy occlusions have been improved. The experimental results obtained for challenging benchmark datasets demonstrate that our approach outperforms state-of-the-art methods in the literature. Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

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OBB-based 4D Manipulation of Traffic Sensory Data for Generating Corner Driving Scenarios

OBB-based 4D Manipulation of Traffic Sensory Data for Genera...

引用

2019 IEEE International Conference on Unmanned Systems, ICUS 2019

作者： Liu, Yu Zhang, Chi Li, Yaochen Li, Li Liu, Yuehu Zheng, Nanning Xi'An Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an China Xi'An Jiaotong University School of Software Engineering Xi'an China Tsinghua University Department of Automation Beijing China

ISBN: (纸本)9781728137926

To validate the intelligent vehicles' cognitive ability sufficiently and efficiently in an off-line test, sensor data of corner scenarios like dangerous driving are necessary and of great importance. However, such scenarios are not easy to be encountered and recorded due to their rare and unpredictable characteristics. In this paper, we propose to manipulate on the multi-sensor data temporarily and spatially, i.e. 4D manipulation, to generate these corner driving scenarios. In order to make manipulation more convenient and accurate, we introduce oriented bounding box (OBB) representing manipulation space. With the cross-modal consistency of existing data, we change contents of semantic images while manipulating contents of point cloud, and then new realistic image sequences of corner driving scenarios are generated with the help of image translation network. However, the newly generated images may exist wrong visual relationships because of the absence of road geometric constraints. To avoid this, in this paper, we propose a gradual construction of bounding box for regional road area method to add road geometric constraints. Finally, in experiments, our proposed 4D manipulation method is validated by generating corner driving scenarios. With the assistance of road geometric constraints, the visual effect of generated images is improved. Besides, experimental results prove that our generated corner driving scenarios impair the performance of the vehicle detection algorithm and are challenging for intelligent vehicle's cognitive ability, with the quality of generated scenarios nearly unchanged. © 2019 IEEE.

关键词： Intelligent vehicle highway systems

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